自编码
涡轮机
故障检测与隔离
风力发电
异常检测
特征提取
断层(地质)
计算机科学
编码器
数据建模
特征向量
编码(内存)
工程类
模式识别(心理学)
人工智能
汽车工程
可靠性工程
深度学习
航空航天工程
电气工程
数据库
地震学
地质学
执行机构
操作系统
作者
Wenliao Du,Zhen Guo,Chuan Li,Xiaoyun Gong,Ziqiang Pu
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:71: 1-10
被引量:18
标识
DOI:10.1109/tim.2022.3187737
摘要
As vital renewable energy devices, wind turbines suffer from gearbox failures due to harsh speed increasing operations. Therefore, the gearbox fault diagnosis is crucial for wind turbine maintenance with reducing economic costs. However, obtaining faulty data is rather challenging, especially at the early fault stage. For this reason, a sparse isolation encoding forest (SIEF) is proposed aiming at both anomaly detection and novel fault discrimination for wind turbine gearboxes. In the present SIEF method, a sparse autoencoder is first trained with only normal data to obtain an optimized and robust weight structure. Newly acquired data corresponding to faulty or healthy conditions are sent to this encoder for feature extraction by encoding to its low dimensional space. All the data in low-dimensional space are fed to an isolation forest for anomaly detection and novel fault discrimination. In the addressed SIEF approach, only normal data are required to train the model for fault detection and further discrimination. It is consistent with the actual operations of the wind turbines, with much less dependence on the fault data for the model training. The proposed method was evaluated by fault diagnosis tests on the wind turbine gearboxes. Results show good performances of the proposal compared to peers.
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